Some previous work [1]}, [2]} showed that extra spatial structure information can lead to a great improvement in image quality. The method in [2]} uses key points and bounding boxes to determine what and where to draw in the image. They represented the key points and bounding boxes as a feature map to feed them into the generator. But labelling key points and bound boxes need a lot of human labour and key points are very subjective. Although they also tried to generate key points with an extra GAN, key points are still needed during training.
Similar to [2]}, Wang \(et\ al\) . [5]} generated images conditioned on surface maps which can reflect rough sketches of the original images. They also use an extra GAN to synthesize surface maps. The above two models learn to generate handcraft annotations so that they can synthesize images from scratch with an explicit notion of image spatial structure. But they both need additional manual annotations to train their model.
